Analysis apparatus and analysis method

ABSTRACT

According to one embodiment, an analysis apparatus includes an acquisition unit and a processor. The acquisition unit acquires first information with a first time length between a first time and a second time. The first information is based on motion of an object person. The processor extracts multiple similarity points from the first information. The multiple similarity points are similar to each other in the first information. The processor calculates a time interval between the similarity points.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.16/444,150, filed Jun. 18, 2019, which is a divisional of and claims thebenefit of priority from U.S. application Ser. No. 15/348,203, filedNov. 10, 2016, which is based upon and claims the benefit of priorityfrom Japanese Patent Application No.2015-221145, filed on Nov. 11, 2015;the entire contents of both of which are incorporated herein byreference.

FIELD

Embodiments described herein generally relate to an analysis apparatusand an analysis method.

BACKGROUND

In a manufacturing site, various analyses have been adopted to improveefficiency of operations. For example, monitoring operation, measuringoperation time, or recoding operation is carried out and the result isanalyzed. However, such analyses take much time. Thus, an analysisapparatus capable of easily analyzing the operation is desired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view showing a configuration of an analysisapparatus according to a first embodiment;

FIG. 2 is a schematic view showing a person to be analyzed;

FIGS. 3 and 4 are graphs showing an example of information acquired bythe acquisition unit;

FIGS. 5 to 7 are graphs showing an example of information processing bythe processor;

FIGS. 8 and 9 are graphs showing an example of information acquired bythe acquisition unit;

FIGS. 10 to 12 are graphs showing an example of information processingby the processor;

FIG. 13 is a schematic view showing an analysis apparatus 110 accordingto a modification of the first embodiment; and

FIG. 14 is a schematic view showing a configuration of an analysisapparatus according to a second embodiment.

DETAILED DESCRIPTION

According to one embodiment, an analysis apparatus includes anacquisition unit and a processor. The acquisition unit acquires firstinformation with a first time length between a first time and a secondtime. The first information is based on motion of an object person. Theprocessor extracts multiple similarity points from the firstinformation. The multiple similarity points are similar to each other inthe first information. The processor calculates a time interval betweenthe multiple similarity points.

First Embodiment

FIG. 1 is a schematic view showing a configuration of an analysisapparatus 100 according to a first embodiment.

As shown in FIG. 1, the analysis apparatus 100 comprises an input unit10, an acquisition unit 20, a processor 30, a display unit 40, and astorage unit 50.

The input unit 10 is, for example, a keyboard, or a touch panel of asmartphone or a tablet. The user of the analysis apparatus 100 inputsinformation into the processor 30 using the input unit 10.

The acquisition unit 20 acquires information based on motion of a personto be analyzed. Length of time for which the acquisition unit 20acquires information may be set using the input unit 10. Or the startand the end of the acquisition may be input by the person to beanalyzed. The acquisition unit 20 outputs the acquired informationtoward the processor 30.

The acquisition unit 20 is, for example, a camera including a depthsensor. In such a case, the acquisition unit 20 is mounted where theperson to be analyzed stays. The acquisition unit 20 acquiresinformation relating to the motion of the person to be analyzed byrecording the person.

Or, the acquisition unit 20 may be an acceleration sensor. In such acase, the acquisition unit 20 is worn on the arm or the leg of theperson to be analyzed. The acquisition unit 20 acquires informationrelating to the motion of the person by sensing the acceleration of thebody part.

The processor 30 processes the information received from the input unit10 and the acquisition unit 20. The processor 30 is, for example, a CPUthat stores software.

The storage unit 50 stores information. The processor 30 can refer tothe storage unit 50 and extract necessary information.

The analysis apparatus 100 may not comprise the storage unit 50. In sucha case, the processor 30 is connected to a hard disk of anotherapparatus, etc., via a network.

The display unit 40 visualizes the information that is output from theprocessor 30 and displays it to the user of the analysis apparatus 100.The display unit 40 is, for example, a liquid crystal display of asmartphone, a tablet, or a PC, etc.

The analysis apparatus 100 comprising such configuration is, forexample, used to analyze motion of an operator in a manufacturing site.For example, in the case where the operator performs predeterminedoperation repeatedly, the analysis apparatus 100 is capable ofextracting the required time for the operation by collecting informationbased on the motion of the operator and by analyzing the information. Atthis time, the analysis apparatus 100 is also capable of storing therequired time to the storage unit 50 and displaying the required time onthe display unit 40.

An example of the operation of the acquisition unit 20 and the processor30 will now be specifically described referring to FIG. 2 to FIG. 12.The case where the acquisition unit 20 includes a depth sensor will nowbe described.

FIG. 2 is a schematic view showing a person to be analyzed.

FIGS. 3, 4, 8, and 9 are graphs showing an example of informationacquired by the acquisition unit 20.

FIGS. 5 to 7 and FIGS. 10 to 12 are graphs showing an example ofinformation processing by the processor 30.

In FIGS. 3, 4, 8, and 9, the horizontal axis indicates the time and thevertical axis indicates the coordinate.

In FIGS. 5, 6, 10, and 11, the horizontal axis indicates the time andthe vertical axis indicates the distance. In these graphs, it meansthat, as the value of the distance is larger, the distance between twoobjects becomes short and the correlation between them gets strong.

In FIGS. 7 and 12, the horizontal axis indicates the time and thevertical axis indicates the scalar value.

FIG. 2 shows a situation in which the operator M to be analyzed isoperating on the desk D. FIG. 2 shows an example when viewed from theacquisition unit 20. In other words, in the example, the acquisitionunit 20 is mounted directly above the operator M.

The acquisition unit 20 sets the coordinate of the recorded space, forexample, as shown FIG. 2. In the example, when viewed from the operatorM who is facing to the desk D, up and down direction is set as Z-axis,right and left direction is set as X-axis, and front and back directionis set as Y-axis.

The acquisition unit 20 senses a distance between the object and theacquisition unit 20 at each point on the X-Y coordinate plane. In otherwords, the acquisition unit 20 senses a depth at each point on the X-Ycoordinate plane. In the case where the acquisition unit 20 records animage of the operator M shown in FIG. 2, the depth of the head isshallowest. And the depth becomes gradually deeper from the shoulders tothe hands.

The acquisition unit 20 is capable of identifying body parts of theoperator M and detecting the motion of the specific body part by sensingthe depth at each point of the operator M. For example, the acquisitionunit 20 determines the head of the operator M and traces the position ofthe head in the X-axis direction so as to detect the motion of theoperator M.

FIG. 3 shows an example of the result detected by the acquisition unit20. The acquisition unit 20 outputs the result information to theprocessor 30 and the processor 30 analyzes it.

In the example shown in FIG. 3, the acquisition unit 20 acquires firstinformation with a time length T (a first time length) from time t₀(first time) to time t_(n) (second time). The processor 30 extracts partinformation with a time length X (a second time length) from the firstinformation. The time length X is shorter than the time length T. In anexample shown in FIG. 3, the processor 30 extracts the part informationwith the time length X from the time t₀ to the time t₁.

For example, the time lengths T and X are preset by the operator M orthe manager of the manufacturing site. Or the time length T may bedetermined by which the operator M inputs the start and the end of theoperation. In such a case, the time length T is from when the operator Minputs the start of the operation to when the operator M inputs the endof the operation.

Or the time length T may be set or modified by the processor 30appropriately. For example, the operator M or the manger inputs anapproximate period A of the operation into the processor 30 in advance;and the processor 30 may set integral multiple of the period A as thetime length T.

Then, separately from the part information, the processor 30 extractsinformation with the time length X between the time t₀ to the time t_(n)at predetermined time interval (a first time interval) from the firstinformation. Specifically, as shown by the arrows in FIG. 4, theprocessor 30 extracts multiple information with the time length X fromthe first information while shifting the range by one frame between thetime t₀ and the time t_(n). It is noted that only a part of multipleinformation extracted by the processor 30 are indicated by the arrows inFIG. 4.

Hereinbelow, each of the multiple information extracted by the stepshown in FIG. 4 is called comparison information.

Then, the processor 30 calculates a distance between the partinformation that is extracted in the step shown in FIG. 3 and each ofthe multiple comparison information that is extracted in the step shownin FIG. 4. The processor 30 calculates, for example, Dynamic TimeWarping (DTW) distance between the part information and each of themultiple comparison information. The processor 30 can obtain thestrength of correlation between them using the DTW distance, regardlessof the variation of the motion period of the operator M. Consequently,information of the distance between the part information and the firstinformation at each time can be obtained. The information of thedistances is shown in FIG. 5.

Hereinbelow, the information including the distance at each time iscalled first correlation information.

Then, the processor 30 sets a tentative similarity point on the firstinformation to estimate the actual operation period of the operator M.The specific example of setting the tentative similarity point will nowbe described. As shown in FIG. 5, the processor 30 sets candidate pointsat random within the variation time N. The time after passage of a timelength μ (a third time length) from the time t₀ is set as the center ofthe variation time N. In the example shown in FIG. 5, three candidatepoints are set at random.

The time μ and the variation time N are, for example, preset by themanager of the manufacturing site.

The processor 30 creates multiple data having normal distribution (thirdinformation). The normal distributions respectively have peaks at thecandidate points α₁ to α_(m). Then, the processor 30 calculates acorrelation coefficient (a second correlation coefficient) between thefirst correlation information such as shown in FIG. 5 and each of thenormal distributions. The processor 30 sets the candidate point whichthe correlation coefficient is highest as the tentative similaritypoint. In the example shown in FIG. 5, the candidate point α₂ is set asthe tentative similarity point.

Then, the processor 30 sets multiple candidate points α₁ to α_(m) on thefirst information within the variation time N again. At this time, thetime after passage of the time μ from the last tentative similaritypoint (the candidate point α₂) is set as the center of the variationtime N. By repeating the steps until the time t_(n), the multipletentative similarity points β₁ to β_(k) are set between the time t₀ andthe time t_(n), as shown in FIG. 6.

Then, the processor 30 creates data including normal distributions thatrespectively have the peaks at the tentative similarity points β₁ toβ_(k), as shown in FIG. 7. Hereinbelow, data including the normaldistributions such as shown in FIG. 7 is called second comparisoninformation.

The processor 30 calculates the correlation coefficient (a firstcorrelation coefficient) between the first correlation information suchas shown in FIG. 5 and FIG. 6 and the second comparison information suchas shown in FIG. 7.

Then, the processor 30 executes the same steps as shown in FIG. 3 toFIG. 7 to the other part information. FIG. 8 to FIG. 12 show theprocessing by the processor 30. It is noted that only information afterthe time t₁ is depicted in FIG. 8 to FIG. 12.

For example, as shown in FIG. 8, the processor 30 extracts the partinformation with the time length X between the time t₁ and the time t₂.Then, as shown in FIG. 9, the processor 30 extracts multiple comparisoninformation with the time length X. The processor 30 creates firstcorrelation information as shown in FIG. 10 by calculating a distancebetween the part information and each of the multiple comparisoninformation.

Then, as shown in FIG. 10, the processor 30 extracts the tentativesimilarity point β by setting candidate points α₁ to α_(m) based on thetime after passage of the time length μ from the time t₁. By repeatingthe steps, the tentative similarity points β₁ to β_(k) are set as shownin FIG. 11.

Then, as shown in FIG. 12, the processor 30 creates second comparisoninformation based on the tentative similarity points β₁ to β_(k). Theprocessor 30 calculates a correlation coefficient between the firstcorrelation information such as shown in FIG. 10 and FIG. 11 and thesecond comparison information such as shown in FIG. 12.

The processor 30 calculates the correlation coefficient for each of themultiple part information by repeating the steps as described aboveafter the time t₂. Multiple groups of the tentative similarity points β₁to β_(k) are created by repeating these steps. The processor 30 extractsone group of the tentative similarity points β₁ to β_(k) that has thehighest correlation coefficient as the true similarity points. Theprocessor 30 obtains the operation period of the operator M bycalculating the time intervals between the true similarity points. Forexample, the processor 30 calculates the average of time lengths betweenadjacent true similarity points and determines the average time as theoperation period.

An example using the information acquired by the depth sensor isdescribed above. However, the embodiment of the invention is not limitedto the example. The operation period of the operator M can be obtainedby acquiring acceleration information of the body part of the operator Mand analyzing the acceleration information in the same way. In addition,the analysis apparatus 100 according to the embodiment can be widelyapplied to estimate the period not only for an operation in amanufacturing site but for an object that repeats specific motion.

According to the analysis apparatus 100, it is possible to automaticallyanalyze motion period of a person to be analyzed. For example, asdescribed above, it is possible to automatically analyze the operationperiod of the operator M in the manufacturing site. Therefore, theanalysis of the operation can be easily performed, because it is notnecessary for the operator to record or report for the analysis and notnecessary for a technical staff to monitor or measure the operation toimprove the efficiency. In addition, it is possible to obtain a periodwith higher accuracy because the analysis result does not depend onexperience, knowledge, or assessment of the person who analyzes it.

The analysis apparatus 100 may comprise multiple acquisition units 20.In such a case, the acquisition units 20 may respectively acquireinformation of mutually-different operators. Or the acquisition units 20may acquire information of one operator.

The analysis apparatus 100 may execute the analysis described above formultiple information which the starts of the time length T are differentfrom each other. For example, in the case where the time length of theinformation to be analyzed is long, the analysis apparatus 100 dividethe whole information into multiple information with shorter timelengths and execute the analysis for each of the multiple information.In such a case, the shorter time length after divided corresponds to thetime length T. By applying such a method, it is possible to decreaserequired time for the analysis processing. It is also possible toanalyze the divided information when the time length T passes so as toreflect the result in the operation while the operator is operating.

Modification

FIG. 13 is a schematic view showing an analysis apparatus 110 accordingto a modification of the first embodiment.

As shown in FIG. 13, the analysis apparatus 110 further includes atransmitter 60 and a receiver 70 in comparison with the analysisapparatus 100.

The transmitter 60 emits a signal including an ID of the transmitter 60.For example, the transmitter 60 is set so as to emit the signal atpredetermined intervals. The signal emitted from the transmitter 60 maybe directional or may be nondirectional.

The receiver 70 receives the signal emitted from the transmitter 60. Thereceiver 70 outputs the signal received from the transmitter 60 towardthe processor 30.

The transmitter 60 is, for example, a beacon, a Radio FrequencyIDentifier (an RFID), or the like provided in a manufacturing site. Orthe transmitter 60 may be a beacon, an RFID, or the like thataccompanies a processing material, a jig, or the like that is not fixed.

The receiver 70 is, for example, a tablet, a smartphone, or the likethat can receive the signal emitted from the transmitter 60 viaBluetooth (registered trademark). In the case where the transmitter 60is an RFID, an RF reader is used as the receiver 70. For example, theoperator that executes the process in the manufacturing site possessesthe receiver 20.

Or the operator may possess the transmitter 60. In such a case, thereceiver 70 is provided in a manufacturing site or at manufacturingequipment. Or the transmitter 60 may accompany a processing material ora jig and the receiver 70 is provided in a manufacturing site or atmanufacturing equipment.

The processor 30 converts the signal input from the receiver 70 intoinformation. Thereby, the processor 30 obtains information, such as anID of the transmitter 60, included in the signal emitted from thetransmitter 60.

The storage unit 50 stores, for example, information such as processflows in the manufacturing site, processes included in the process flow,manufacturing equipment used in each process, a location where eachprocess is executed, an operator that executes each process, an objectprocessed by each process, etc.

The storage unit 50 also stores information related to locations orholders of the transmitter 60 and the receiver 70. For example, in thecase where the transmitter 60 is provided in a manufacturing site, thestorage unit 50 stores information on such as the process executed atthe location where the transmitter 60 is provided, the processingmaterial, the equipment used for the process, etc.

Therefore, the processor 30 can extract information related to the ID ofthe transmitter 60 from the storage unit 50 by collating the ID of thetransmitter 60 input from the receiver 70 and by referring to theinformation related to the ID in the storage unit 50.

For example, the analysis apparatus 110 according to the modificationacquires the information of the operation executed by the operator M,with using the transmitter 60 and the receiver 70; and it is possible tomanage the information of the operation in association with theoperation period.

Second Embodiment

FIG. 14 is a schematic view showing a configuration of an analysisapparatus 200 according to a second embodiment.

As shown in FIG. 14, the analysis apparatus 200 further includes animaging unit 80 in comparison with the analysis apparatus 100.

The imaging unit 80 records an image of the operator M; and theprocessor 30 identifies the operator M based on the recorded image. Inthe case where the acquisition unit 20 is a depth sensor, the processor30 can identify the operator M based on the image recorded by theacquisition unit 20. In other words, one component may comprise bothfunctions of the acquisition unit 20 and the imaging unit 80. Thestorage unit 50 stores information necessary to identify the operator M.

The following methods can be used to identify the operator M based onthe recorded image. It is possible to combine two or more methodsdescribed below.

In the first method, skeleton information extracted from the recordedimage is used for the identification.

In this method, the processor 30 extracts joint parts of the skeletonsuch as the shoulder, elbow, waist, knee, etc., and end parts of theskeleton such as the head, hand, foot, etc., of the operator M from therecorded image. Then, the processor 30 acquires skeleton information bylinking the joint parts and the end parts. The processor 30 calculatesthe breadth of the shoulders, the lengths of the right and left upperarms, and the lengths of the right and left forearms based on theskeleton information. The processor 30 identifies the operator M bycollating the calculated result to the information stored in the storageunit 50.

In the second method, the area of the specific body part of the operatorM is used for the identification.

In this method, the processor 30 calculates the area of the specificbody part of the operator M based on the recorded image. Then, theprocessor 30 collates the calculated result to the information stored inthe storage unit 50 so as to identify the operator M. For example, theprocessor 30 identifies the operator M using the area of the rightshoulder, the area of the left shoulder, or the sum of these areas.

In the third method, the posture of the operator M is used for theidentification.

In this method, the processor 30 detects the posture of the operator Mbased on the recorded image. Specifically, the processor 30 acquiresskeleton information in the same way as the first method. At the sametime, the processor 30 detects the posture of the operator M such as theangle of the neck using the depth information of the recorded image. Theprocessor 30 identifies the operator M by collating the detected postureto the information stored in the storage unit 50.

In the fourth method, the shape of the specific body part of theoperator M is used for the identification.

In this method, the processor 30 detects the shape of the specific bodypart of the operator M based on the depth information. The processor 30identifies the operator M by collating the detected shape to theinformation stored in the storage unit 50.

In the four methods exemplified above, an image of the operator M thatis recorded from the front, the rear, the upper, or the side may be usedto identify the operator.

The fourth method of the exemplified four methods will now be described.Specifically, the case where the operator M is identified based on theshape of the head will now be described. It is possible to execute sameprocessing for the other body part of the operator M.

First, data stored in the storage unit 50 will be described. The datamay be called training data.

The storage unit 50 stores data relating to multiple operators who havepossibility to be analyzed. The storage unit 50 also stores anidentifier to discriminate each operator from the others.

As a specific example, the storage unit 50 stores data relating to threeoperators of a first operator, a second operator, and a third operator.In such a case, the storage unit 50 stores data relating to the headshapes of the operators. The data includes multiple head shape for eachoperator. The data relating to the head shapes of each operator is, forexample, an image recorded by the imaging unit 80. In other words, imagedata which the imaging unit 80 had recorded the head of the operatorfrom directly above is stored in the storage unit 50.

The data of the head shape is converted, for example by Point Cloud,into characteristics so as to calculate the distance between the headshapes.

The storage unit 50 stores a distance between the first operator'scharacteristics is stored in the storage unit 50. The storage unit 50also stores a distance between the first operator's characteristic andthe second operator's characteristic and a distance between the firstoperator's characteristic and the third operator's characteristic. Anidentifier, for example derived by Support Vector Machine (SVM),multiple Kernel SVM, random forest, or neural net, is set for thesedistances. The distance between the first operator's characteristics andthe distance between the first operator's characteristic and the otheroperator's characteristic are discriminated by the identifier.

Similarly, for each of the second operator and the third operator, thestorage unit 50 stores the distance between the operator'scharacteristics and the distance between the operator's characteristicand the other operator's characteristic; and identifiers derived bymultiple Kernel SVM are set for these distances.

The processor 30 identifies the operator M based on the information thatis stored in the storage unit 50.

First, the processor 30 extracts data of a head shape of the operator Mfrom the recorded image and converts the data into a characteristic. Atthis time, the processor 30 extracts multiple data from multiple imagesof the operator M and converts each of the multiple data into acharacteristic. Then, the processor 30 removes an outlier from theobtained characteristics. For example, removing the outlier is performedby Agglomerative Hierarchical Clustering (AHC) method.

Then, the processor 30 selects multiple characteristics of the operatorM and multiple characteristics of the first operator; and the processor30 calculates the distances between the characteristics of the operatorM and the characteristics of the first operator for multiplecombinations. Continuing in the step, the processor 30 calculates amargin of each of the calculated distances to the identifier thatdiscriminates the first operator from the other operators. Then theprocessor 30 calculates the sum of the margins.

Similarly, for the second operator and the third operator, the processor30 calculates the margins to the identifiers that discriminate thesecond operator or the third operator from other operators; and theprocessor 30 calculates the sums of the margins.

Then the processor 30 determines an operator whom the highest margin isobtained as the operator M.

According to the embodiment, it is possible to automatically calculatethe motion period of the person to be analyzed and identify the person.Therefore, when the period is calculated, it is not necessary tomanually identify the analyzed person. For example, in a productionsite, it is possible to manage the operating period and the operatorwith automatically combining them.

It is also possible that the transmitter 60 and the receiver 70,described in the modification of the first embodiment, can be applied tothe second embodiment. By such a configuration, it is possible toautomatically acquire the operation period and information relating to,for example, the operator M and the operation. Therefore, it is possibleto manage more information with automatically combining them.

Note

An analysis apparatus, comprising:

-   -   an acquisition unit acquiring first information with a first        time length T between a first time t₀ and a second time t_(n),        the first information being based on motion of an object person;        and    -   a processor configured to execute        -   a first step of extracting a plurality of second information            (part information) with a second time length X from the            first information at mutually-different times, the second            time length X being shorter than the first time length T as            shown in FIG. 3,        -   a second step of extracting a plurality of third information            (first comparison information) with the second time length X            from the first information, the plurality of third            information being extracted between the first time t₀ and            the second time t_(n) at first time intervals (e.g., one            frame) as shown in FIG. 4,        -   a third step of calculating distances between one of the            plurality of second information (part information) and each            of the plurality of third information (first comparison            information) to create fourth information (first correlation            information), the fourth information (first correlation            information) including distances between the one of the            plurality of second information (part information) and the            first information at times that respectively correspond to            the plurality of third information (first comparison            information), wherein the times respectively corresponding            to the plurality of third information may have one frame            intervals,        -   a fourth step of setting a plurality of candidate points α₁            to α_(m) near a time after passage of a third time length μ            from the first time t₀,        -   a fifth step of creating a plurality of fifth information            (third comparison information), the plurality of the fifth            information (the third comparison information) having peak            at the respective candidate points α₁ to α_(m),        -   a sixth step of calculating a second correlation coefficient            between each of the plurality of the fifth information (the            third comparison information) and the fourth information            (the first correlation information),        -   a seventh step of setting the candidate point which the            second correlation coefficient is highest as a tentative            similarity point β₁;        -   an eighth step of repeating the set of other candidate            points near a time after passage of the third time length μ            from the last tentative similarity point and executing the            fifth step to the seventh step for the other candidate            points to set other tentative similarity points β₂ to β_(k)            as shown in FIG. 6, and        -   a ninth step of calculating a first correlation coefficient            between the fourth information (the first correlation            information) and sixth information (second comparison            information) that is created based on the plurality of            tentative similarity points β₁ to β_(k),    -   the processor repeating the third step to the ninth step for        each of the other mutually-different second information (part        information) extracted in the first step, determining one group        of the plurality of tentative similarity points β₁ to β_(k)        corresponding to the fourth information (the first correlation        information) which the first correlation coefficient is highest,        and calculating time intervals between the one group of the        plurality of tentative similarity points β₁ to β_(k).

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the invention. Moreover, above-mentioned embodiments can becombined mutually and can be carried out.

What is claimed is:
 1. An analysis apparatus comprising: an acquisitionunit acquiring first information with a first time length between afirst time and a second time, the first information being based onmotion of an object person; and a processor extracting a plurality ofsimilarity points from the first information, the plurality ofsimilarity points being similar to each other in the first information,the processor calculating a time interval between the similarity points.2. The analysis apparatus according to claim 1, wherein the processorextracts part information with a second time length from the firstinformation, the second time length is shorter than the first timelength, and extracts the plurality of similarity points using a distancebetween the first information and the part information.
 3. The analysisapparatus according to claim 2, wherein the processor extracts aplurality of first comparison information from the first informationbetween the first time and the second time at a first time interval,calculates each distance between each of the plurality of the firstcomparison information and the part information to create firstcorrelation information that includes a distance between the firstinformation and the part information at each time, sets a plurality oftentative similarity points based on the first correlation information,calculates a first correlation coefficient between the first correlationinformation and second comparison information that is created based onthe plurality of tentative similarity points, and determines asuitability of the plurality of tentative similarity points using thefirst correlation coefficient.
 4. The analysis apparatus according toclaim 3, wherein the processor sets a plurality of candidate points thatare candidates for the tentative similarity point, the plurality ofcandidate points are set near a time after a third time length from thefirst time, creates a plurality of third comparison information, theplurality of third comparison information respectively have peaks at theplurality of candidate points, calculates each second correlationcoefficient between each of the plurality of the third comparisoninformation and the first correlation information, sets one of thecandidate points which the second correlation coefficient is highest asthe tentative similarity point, and sets the plurality of similaritypoints by repeating set of other candidate points near a time after athird time length from the set tentative point.
 5. The analysisapparatus according to claim 4, wherein the processor extracts aplurality of part information with the second time length from the firstinformation at mutually-different time, calculates the first correlationcoefficient for each of the plurality of the part information, andextracts the plurality of tentative similarity points, based on thefirst correlation information which the first correlation coefficient ishighest, as the plurality of similarity points.
 6. The analysisapparatus according to claim 1, wherein the acquisition unit is anacceleration sensor worn by the object person, and the first informationincludes acceleration information of a body part of the object person.7. The analysis apparatus according to claim 1, wherein the acquisitionunit is capable of acquiring depth information when recording, and theacquisition unit acquires the first information by detecting motion of abody part of the object person using the depth information.
 8. Theanalysis apparatus according to claim 1, further comprising an imaginingunit recording the object person, the imaging unit acquiring depthinformation when recording, the processor extracting a head shape of theobject person from the depth information to identify the object person.9. The analysis apparatus according to claim 8, further comprising astorage unit, the storage unit storing data that includes head shapes ofa plurality of persons from a first person to an Nth person, N is aninteger equal to or greater than 2, the storage unit storing a pluralityof identifies from a first identifier to an Nth identifier, the Mthidentifier being used to discriminate the Mth person from the otherpersons, M is an integer not less than 1 and not more than N, theprocessor identifying the object person using the extracted head shapeand the plurality of identifiers.
 10. The analysis apparatus accordingto claim 9, wherein the processor calculates each distance between theextracted head shape of the object person and each of the head shapes ofthe persons from the first person to the Nth person, calculatesrespective margins between the distances and the identifiers from thefirst identifier to the Nth identifier, and determines the personcorresponding to the identifier which the calculated margin is highestas the object person.
 11. The analysis apparatus according to claim 10,wherein the processor associates the time interval with the persondetermined as the object person.
 12. An analysis apparatus, comprising:an imaging unit recording an image of an object person and acquiringdepth information; and a processor extracting a head shape of the objectperson from the image using the depth information, and identifying theobject person based on the head shape.
 13. The analysis apparatusaccording to claim 12, further comprising a storage unit, the storageunit storing data that includes head shapes of a plurality of personsfrom a first person to an Nth person, N is an integer equal to orgreater than 2, the storage unit storing a plurality of identifies froma first identifier to an Nth identifier, the Mth identifier being usedto discriminate the Mth person from the other persons, M is an integernot less than 1 and not more than N, the processor calculating eachdistance between the extracted head shape of the object person and eachof the head shapes of the persons from the first person to the Nthperson, calculating respective margins between the calculated distancesand the identifiers from the first identifier to the Nth identifier, anddetermining the person corresponding to the identifier which thecalculated margin is highest as the object person.
 14. An analysismethod comprising: a first step of acquiring first information based onmotion of an object person, the first information with a first timelength between a first time and a second time; a second step ofextracting a plurality of part information with a second time lengthfrom the first information at mutually-different times, the second timelength being shorter than the first time length; a third step ofextracting a plurality of first comparison information with the secondtime length from the first information, the plurality of thirdinformation being extracted between the first time and the second timeat first time intervals; a fourth step of calculating distances betweenone of the plurality of part information and each of the plurality offirst comparison information create first correlation information, thefirst correlation information including distances between the one of theplurality of part information and the first information at timesrespectively corresponding to the plurality of first comparisoninformation; a fifth step of setting a plurality of candidate pointsnear a time after passage of a third time length from the first time; asixth step of creating a plurality of third comparison information, theplurality of the third comparison information having peak at therespective candidate points; a seventh step of calculating a secondcorrelation coefficient between each of the plurality of the thirdcomparison information and the first correlation information; an eighthstep of setting the candidate point which the second correlationcoefficient is highest as a tentative similarity point; a ninth step ofrepeating the set of other candidate points near a time after passage ofthe third time length from the last tentative similarity point andexecuting the sixth step to the eighth step for the other candidatepoints to set other tentative similarity points; and a tenth step ofcalculating a first correlation coefficient between the firstcorrelation information and second comparison information that iscreated based on the plurality of tentative similarity points, repeatingthe fourth step to the tenth step for each of the othermutually-different part information extracted in the second step,determining one group of the plurality of tentative similarity pointscorresponding to the first correlation information which the firstcorrelation coefficient is highest, and calculating time intervalsbetween the one group of the plurality of tentative similarity points.15. The analysis method according to claim 14, wherein the firstinformation includes acceleration information of a body part of theobject person.
 16. The analysis method according to claim 14, whereinthe first information includes a detected result of motion of a bodypart of the object person, the detection is based on depth informationacquired by recording the object person.
 17. The analysis methodaccording to claim 14, further comprising: an eleventh step of acquiringdepth information by recording the object person; a twelfth step ofextracting a head shape of the object person from the depth information;a thirteenth step of using data that relates to head shapes of aplurality of persons from a first person to an Nth person and aplurality of identifiers from a first identifier to an Nth identifier soas to calculate distances between the extracted head shape of the objectperson and the head shapes of the plurality of persons, N is an integerequal to or greater than 2, the Mth identifier being used todiscriminate the Mth person from other persons, M is an integer not lessthan 1 and not more than N; a fourteenth step of calculating respectivemargins between the calculated distances and the plurality ofidentifiers from the first identifier to the Nth identifier; and afifteenth step of determining the person corresponding to the identifierwhich the calculated margin is highest as the object person.
 18. Theanalysis method according to claim 17, further comprising a sixteenthstep of associating the time interval with the person determined as theobject person.